Introduction

Our group members are Ziyi Bai, Yinfeng Zhou and Congyao Duan. This is our group report about what we have done for this mapping project.

Step1: Data Cleaning

Our dataset is based on the FEMA Dataset, which is about Public Assistance Funded Projects Details. We filtered the data about storm from 1998-8-26 to 2020-3-30 and added information about longitude and latitude into the dataset. Our dataset contains 562497 rows and 14 variables.

The following image is an indicator about our dataset.

data

Full process of data cleaning: you may access our code from github

data cleaning data cleaning data cleaning

Setp 2: Summary of our dataset

public <- read.csv("public.csv")
head(public)
##   X  region subregion fips disasterNumber     declarationDate    incidentType
## 1 1 alabama   autauga 1001           1971 2011-04-28 04:00:00 Severe Storm(s)
## 2 2 alabama   autauga 1001           1971 2011-04-28 04:00:00 Severe Storm(s)
## 3 3 alabama   autauga 1001           1971 2011-04-28 04:00:00 Severe Storm(s)
## 4 4 alabama   autauga 1001           1971 2011-04-28 04:00:00 Severe Storm(s)
## 5 5 alabama   autauga 1001           1971 2011-04-28 04:00:00 Severe Storm(s)
## 6 6 alabama   autauga 1001           1971 2011-04-28 04:00:00 Severe Storm(s)
##        long      lat group order   yy mm dd
## 1 -86.85468 32.44087     1    34 2011  4 28
## 2 -86.43641 32.41222     1    47 2011  4 28
## 3 -86.61403 32.38931     1     7 2011  4 28
## 4 -86.83175 32.42368     1    32 2011  4 28
## 5 -86.82603 32.38931     1    30 2011  4 28
## 6 -86.83748 32.43514     1    33 2011  4 28
summary(public$long)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -124.68  -94.37  -87.30  -88.87  -80.76  -67.01
summary(public$lat)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   25.13   33.77   37.38   37.46   41.03   49.38
library(ggplot2)
save <- read.csv("save.csv")
ggplot(save,aes(x=incidentType))+geom_histogram(position="identity",stat="count",fill="lightblue", color="black")+
  theme(axis.text.x = element_text(angle = 60, hjust = 1),
        axis.text = element_text(size = 7),
        axis.title = element_text(size = 13, face = "bold"))
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Step 3: Interaction shiny app

Feel free to access our shiny app.

We made an interaction shiny app to show which area of the United States had explored which type of incident. In our shiny app, you are able to drag and zoom in our map to take a closer look of the whole country map. Also, we have a sidebar which used to select the incident type, year, month and day. After you select them all, the corresponding area will become shaded and keep flashing to notice you. Following are some examples of our shiny app.

This one shows you the area that exposed to Coastal Storm in 2001-6-9.

shiny

This one shows you where experienced Terrorist in 2013-4-17.

shiny

You are able to explore more information in our shiny app!

Step 4: Presentation

Feel free to access our presentation document

Thanks

Thanks this html example contributor